Over the past few years, many UDA methods have emerged, which can be roughly categorized as two types: 1) difference-based methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, which explicitly mitigate inter-domain difference by minimizing statistical metrics such as maximum mean discrepancy, joint maximum mean discrepancy, margin disparity discrepancy, and optimal transport distance; and 2) adversarial-based methods [11]}, [12]}, [13]}, [14]}, which introduce a discriminator to adversarially learn domain-invariant representations by obfuscating domain-specific information.
These methods devote to strengthening the feature transferability between domains to alleviate the problem of inter-domain differences. However, under the UDA conditions, for the former, the feature extractor may incorrectly draw the distance of different classes closer, resulting in a decline in class recognition ability, i.e. a decrease in discriminability; for the latter, the discriminator forces the feature extractor to learn domain-invariant representations, while ignoring the learning of class-variant representations, which may also lead to a decline in discriminability. This decline in discriminability is ineviTable. Since the target domain is completely unlabeled, when the domain gap is large, the class-discriminative information is easily lost when performing domain alignment, and the classifier remains inevitably biased towards the source domain [13]}, [16]}, leading to unexpected deterioration of discriminability in the target domain. Based on the above observations, our work focuses on designing a more robust solution to simultaneously enhance feature transferability and discriminability to further reduce the domain gap.
